Dr Richard Price
Lead Data Scientist, Planning and Analytical Services Information and Digital Services Division
11:00 AM The Use of Predictive Analytics in Learning Analytics to Maximize Student Success and Retention
There is tremendous potential in the data collected to help identify student behaviours that lead to positive and conversely negative student outcomes. Learning Analytics algorithms have the ability to predict whether a student is likely to pass or fail in their chosen units to consequently inform early intervention strategies aimed at preventing problems before they occur and in turn reduce the attrition rate. In this session, Richard Price will provide examples of behavioral modelling algorithms he and his colleagues at Flinders University have developed that have been designed for this purpose.
This session will show you:
- Tools and algorithms designed for the early identification of students at risk
- Discuss whole of university support strategies for identified at-risk students
- How learning analytics has the potential to advise and assist students on strategies to help them maximize their chances of success